Citation: Li, S.; Qiu, Y.; Jiang, J.;
Wang, H.; Nan, Q.; Sun, L.
Identification of Abnormal Vibration
Signal of Subway Track Bed Based on
Ultra-Weak FBG Sensing Array
Combined with Unsupervised
Learning Network. Symmetry 2022,
14, 1100. https://doi.org/10.3390/
sym14061100
Academic Editor: Igor V. Andrianov
Received: 10 May 2022
Accepted: 25 May 2022
Published: 27 May 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Identification of Abnormal Vibration Signal of Subway Track
Bed Based on Ultra-Weak FBG Sensing Array Combined with
Unsupervised Learning Network
Sheng Li
1
, Yang Qiu
2
, Jinpeng Jiang
1
, Honghai Wang
1
, Qiuming Nan
1,
* and Lizhi Sun
3
1
National Engineering Research Center of Fiber Optic Sensing Technology and Networks, Wuhan University
of Technology, Wuhan 430070, China; lisheng@whut.edu.cn (S.L.); jiangjp2812@whut.edu.cn (J.J.);
wanghh@whut.edu.cn (H.W.)
2
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China;
qiuyang@whut.edu.cn
3
Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA;
lsun@uci.edu
* Correspondence: nqm0723@whut.edu.cn
Abstract:
The performance of the passing train and the structural state of the track bed are the
concerns regarding the safe operation of subways. Monitoring the vibration response of the track
bed structure and identifying abnormal signals within it will help address both of these concerns.
Given that it is difficult to collect abnormal samples that are symmetric to those of the normal state of
the structure in actual engineering, this paper proposes an unsupervised learning-based methodology
for identifying the abnormal signals of the track beds detected by the ultra-weak fiber optic Bragg
grating sensing array. For an actual subway tunnel monitoring system, an unsupervised learning
network was trained by using a sufficient amount of vibration signals of the track bed collected
when trains passed under normal conditions, which was used to quantify the deviations caused
by anomalies. An experiment to validate the proposed procedures was designed and implemented
according to the obtained normal and abnormal samples. The abnormal vibration samples of the
track beds in the experiment came from two parts and were defined as three levels. One part of it
stemmed from the vibration responses under the worn wheels of a train detected during system
operation. The remaining abnormal samples were simulated by superimposing perturbations in the
normal samples. The experimental results demonstrated that the established unsupervised learning
network and the selected metric for quantifying error sequences can serve the threshold selection
well based on the receiver operating characteristic curve. Moreover, the discussion results of the
comparative tests also illustrated that the average results of accuracy and F1-score of the proposed
network were at least 11% and 13% higher than those of the comparison networks, respectively.
Keywords:
signal anomaly detection; subway track bed; distributed vibration; unsupervised learning
network; attention mechanism; ultra-weak fiber optic Bragg grating
1. Introduction
Generally speaking, in-service engineering structures are always in two symmetrical
operating states, normal and abnormal. Although the probability of occurrence of the
structural abnormal state is relatively low, tracking and monitoring the service status of
subway track beds before catastrophic accidents is of great significance to ensure the safe
operation of trains. The traditional inspection regime is usually labor-intensive and can
be significantly expensive for rail operation management [
1
]. Although various types of
rail inspection vehicles integrated with ultrasonic methods [
2
], eddy current [
3
], infrared
thermography [
4
], laser scanning [
5
], and other non-destructive testing equipment have im-
proved the efficiency of inspection, they still have difficulty meeting the frequent inspection
Symmetry 2022, 14, 1100. https://doi.org/10.3390/sym14061100 https://www.mdpi.com/journal/symmetry
